The problem we had with the perceptron were:
only converged for linearly separable problems
stopped places that did not look like they would generalize well
We need an algorithm that takes a more balanced approach: - finds a "middle ground"
decision boundary - can make decisions even when the data is not separable
for binary classification for positive (y=1) vs negative (y=0) class
for a probability p to belong to the positive class, the odds ratio is given by
if r>1 we are more likely to be in the positive class
if r<1 we are more likely to be in the negative class
to make it more symmetric we consider the log of r
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